Pseudocode – how does it work?

With every technique, we need to understand the baseline algorithm before we can lay down any code. So, in this section, we'll discuss how the training algorithm works.

Getting ready

In this section, we'll be referring to the SimGAN paper once again.

How to do it...

In the SimGAN paper, the authors provided a convenient graphic for users to base their development on. We already know that we need to develop models for each of the networks, but how do we train a network in the first place? The following diagram offers an explanation:

Algorithm 

Let's convert the preceding diagram into the following, tangible steps:

1. Read both synthetic images and real images into variables.
2. Then, for every epoch, do the following:
   • Train the refiner networks on a random mini batch for K_Gtimes
   • Train the discriminator network on a random mini batch for K_D times
3. Stop when the number of epochs reached, or lost, has not changed significantly for nepochs.